This analysis examines two major interventions that reshaped the sports betting landscape: the COVID-19 pandemic (March 2020) and New York’s online sports betting legalization (January 2022). Using Google Trends data spanning 2020-2024, we employ interrupted time series regression to quantify both immediate shocks and sustained trend changes. The ITS model
isolates intervention effects from underlying trends, measuring level changes (\(\beta_2\)) and slope changes (\(\beta_3\)) in search interest. Google Trends provides a direct cultural proxy for public engagement, capturing how these events transformed sports betting from a niche activity during COVID’s sports shutdown to mainstream entertainment following New York’s market entry.
Intervention: New York online sports betting launch (January 8, 2022) - the largest U.S. sports betting market by revenue, representing ~20% of the national market. Research Question: Did NY legalization produce a significant change in public interest in sports betting? We analyze Google search trends as a direct measure of cultural impact and consumer awareness, examining four search terms that capture different facets of betting interest from general (“sports betting”) to operator-specific (“draftkings”) to sport-specific (“nba betting”).
Visualization & Data Preparation
Code
# Visualizationp1 <-ggplot(sports_betting, aes(x = date, y = hits)) +geom_line(color ="#1e88e5", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(sports_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'sports betting'",subtitle ="NY sports betting legalization intervention point marked in red",x ="Date", y ="Search Interest (0-100)" ) +theme(plot.title =element_text(face ="bold"))p2 <-ggplot(draftkings, aes(x = date, y = hits)) +geom_line(color ="#ff6f00", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(draftkings$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'draftkings'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))p3 <-ggplot(online_betting, aes(x = date, y = hits)) +geom_line(color ="#43a047", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(online_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'online betting'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))p4 <-ggplot(nba_betting, aes(x = date, y = hits)) +geom_line(color ="#8e24aa", linewidth =1) +geom_vline(xintercept = intervention_date, color ="red", linetype ="dashed", linewidth =1.2) +annotate("text",x = intervention_date, y =max(nba_betting$hits) *0.95,label ="NY Launch\nJan 8, 2022", color ="red", size =4, fontface ="bold" ) +labs(title ="Google Searches: 'nba betting'", x ="Date", y ="Search Interest (0-100)") +theme(plot.title =element_text(face ="bold"))gridExtra::grid.arrange(p1, p2, p3, p4, ncol =2)
Code
# Data preparation tableprep_table <- sports_betting %>%filter(date >=as.Date("2021-12-01") & date <=as.Date("2022-02-28")) %>%select(date, hits, Time, Post_Intervention, Time_Since_Intervention) %>%mutate(Period =ifelse(Post_Intervention ==0, "Pre-NY", "Post-NY")) %>%select(date, Period, Y = hits, X_t = Time, Z_t = Post_Intervention, P_t = Time_Since_Intervention)kable(prep_table,format ="html", digits =1,caption ="ITS Variables for NY Legalization Analysis (Sample Period)",col.names =c("Date", "Period", "Y (Search Interest)", "X_t (Time)", "Z_t (NY Indicator)", "P_t (Time Since NY)")) %>%kable_styling(full_width =FALSE, bootstrap_options =c("striped", "hover", "condensed")) %>%row_spec(0, bold =TRUE, background ="#2c3e50", color ="white") %>%row_spec(which(prep_table$Z_t ==1), background ="#e3f2fd")
ITS Variables for NY Legalization Analysis (Sample Period)
Date
Period
Y (Search Interest)
X_t (Time)
Z_t (NY Indicator)
P_t (Time Since NY)
2021-12-05
Pre-NY
35
707
0
0
2021-12-12
Pre-NY
35
714
0
0
2021-12-19
Pre-NY
33
721
0
0
2021-12-26
Pre-NY
36
728
0
0
2022-01-02
Pre-NY
43
735
0
0
2022-01-09
Post-NY
46
742
1
1
2022-01-16
Post-NY
40
749
1
8
2022-01-23
Post-NY
37
756
1
15
2022-01-30
Post-NY
35
763
1
22
2022-02-06
Post-NY
40
770
1
29
2022-02-13
Post-NY
45
777
1
36
2022-02-20
Post-NY
23
784
1
43
2022-02-27
Post-NY
24
791
1
50
Variables: Y = Search interest; X_t = Time (weeks from start); Z_t = NY intervention indicator (0 before, 1 after Jan 8, 2022); P_t = Weeks since NY launch (0 before intervention, increases after).
cat(sprintf("\nCounterfactual: Without NY legalization, current search interest would be %.1f instead of %.1f\n",tail(sports_betting$Counterfactual, 1), tail(sports_betting$hits, 1)))
Counterfactual: Without NY legalization, current search interest would be 72.0 instead of 34.0
The immediate effect is captured by the β₂ coefficient, which measures whether search interest jumped or declined right after January 8, 2022. A significant positive value indicates that New York’s legalization produced an instant cultural response. The counterfactual blue dashed line represents what search interest would have looked like had legalization not occurred; the gap between the blue counterfactual and the red actual line quantifies the intervention’s overall impact at any point in time.
The sustained effect is reflected in the β₃ coefficient, which shows whether the growth trajectory changed after legalization. Positive values signal accelerated interest as the New York betting market stabilized and expanded. Beyond this trajectory shift, the delayed effect, seen in both the average post-intervention difference and the current separation between actual and counterfactual, reveals whether the impact persisted long after the initial launch period.
Background & Variable Selection
Intervention: WHO declared COVID-19 pandemic on March 11, 2020, causing immediate suspension of all major sports leagues (NBA, NHL, MLB). Variable: “Sports betting” Google searches - an ITS candidate because the intervention had a clear, direct causal pathway: no live sports = no sports betting.
Visualization & Data Preparation
Code
# Prepare COVID ITS datasports_covid_its <- sports_betting %>%mutate(X_t =row_number(),Z_t =ifelse(date >= covid_intervention, 1, 0),P_t =ifelse(date >= covid_intervention, as.numeric(date - covid_intervention) /7, 0),Y = hits )# Visualizationsports_covid <- sports_covid_its %>%filter(date >=as.Date("2019-12-01") & date <=as.Date("2021-12-31"))ggplot(sports_covid, aes(x = date, y = Y)) +geom_line(color ="#1e88e5", linewidth =1.2) +geom_point(color ="#1e88e5", size =1.5, alpha =0.6) +geom_vline(xintercept = covid_intervention, color ="red", linetype ="dashed", linewidth =1.5) +annotate("text",x = covid_intervention, y =max(sports_covid$Y) *0.95,label ="COVID-19 Pandemic\nMarch 11, 2020\nSports Leagues Shut Down",color ="red", size =5, fontface ="bold", hjust =-0.1 ) +annotate("rect",xmin = covid_intervention, xmax =as.Date("2020-07-01"),ymin =-Inf, ymax =Inf, alpha =0.1, fill ="red" ) +annotate("text",x =as.Date("2020-05-01"), y =max(sports_covid$Y) *0.1,label ="Sports Shutdown Period", color ="darkred", size =4, fontface ="italic" ) +labs(title ="Google Searches for 'Sports Betting' Around COVID-19 Pandemic",subtitle ="Dramatic decline when sports leagues shut down in March 2020",x ="Date", y ="Search Interest (0-100)",caption ="Red shaded area indicates major sports leagues suspended" ) +theme(plot.title =element_text(face ="bold", size =16))
Code
# Data preparation tablecovid_table <- sports_covid_its %>%filter(date >=as.Date("2020-02-01") & date <=as.Date("2020-05-31")) %>%select(date, Y, X_t, Z_t, P_t) %>%mutate(Period =ifelse(Z_t ==0, "Pre-COVID", "Post-COVID"), P_t =round(P_t, 1)) %>%select(date, Period, Y, X_t, Z_t, P_t)kable(covid_table,format ="html", digits =1,caption ="ITS Variables for COVID-19 Analysis (Sample Period)",col.names =c("Date", "Period", "Y (Search Interest)", "X_t (Time)", "Z_t (COVID Indicator)", "P_t (Weeks Since COVID)")) %>%kable_styling(full_width =FALSE, bootstrap_options =c("striped", "hover", "condensed")) %>%row_spec(0, bold =TRUE, background ="#2c3e50", color ="white") %>%row_spec(which(covid_table$Z_t ==1), background ="#ffebee") %>%column_spec(5, bold =TRUE, color =ifelse(covid_table$Z_t ==1, "red", "black"))
ITS Variables for COVID-19 Analysis (Sample Period)
Date
Period
Y (Search Interest)
X_t (Time)
Z_t (COVID Indicator)
P_t (Weeks Since COVID)
2020-02-02
Pre-COVID
29
6
0
0.0
2020-02-09
Pre-COVID
17
7
0
0.0
2020-02-16
Pre-COVID
16
8
0
0.0
2020-02-23
Pre-COVID
17
9
0
0.0
2020-03-01
Pre-COVID
18
10
0
0.0
2020-03-08
Pre-COVID
16
11
0
0.0
2020-03-15
Post-COVID
6
12
1
0.6
2020-03-22
Post-COVID
6
13
1
1.6
2020-03-29
Post-COVID
6
14
1
2.6
2020-04-05
Post-COVID
5
15
1
3.6
2020-04-12
Post-COVID
6
16
1
4.6
2020-04-19
Post-COVID
7
17
1
5.6
2020-04-26
Post-COVID
7
18
1
6.6
2020-05-03
Post-COVID
8
19
1
7.6
2020-05-10
Post-COVID
8
20
1
8.6
2020-05-17
Post-COVID
8
21
1
9.6
2020-05-24
Post-COVID
10
22
1
10.6
2020-05-31
Post-COVID
10
23
1
11.6
Variables: Y = Search interest; X_t = Time index (weeks from dataset start); Z_t = COVID indicator (0 before March 11, 2020; 1 after); P_t = Weeks since COVID intervention (0 before, increases after).
cat(sprintf("Counterfactual: Without COVID, current search interest would be %.1f instead of %.1f\n",tail(sports_covid_its$Counterfactual, 1), tail(sports_covid_its$Y, 1)))
Counterfactual: Without COVID, current search interest would be -288.0 instead of 34.0
Code
ggplot(post_covid_data, aes(x = date, y = Effect)) +geom_line(color ="#d32f2f", linewidth =1.2) +geom_point(color ="#d32f2f", size =2) +geom_hline(yintercept =0, linetype ="dashed", color ="gray30", linewidth =1) +geom_smooth(method ="loess", se =TRUE, color ="#1e88e5", fill ="#1e88e5", alpha =0.2) +labs(title ="Intervention Effect Over Time: COVID-19 Impact Trajectory",subtitle ="Negative values = Interest below counterfactual | Positive = Above counterfactual",x ="Date", y ="Intervention Effect (Predicted - Counterfactual)",caption ="Blue trend line shows recovery trajectory over time" ) +theme(plot.title =element_text(face ="bold", size =15))
The immediate effect is captured by β₂, which shows that search interest dropped roughly %.2 points the moment sports shut down, a catastrophic collapse that wiped out the core activity driving betting demand. The counterfactual trajectory illustrates what search interest would have been without COVID; the gap between actual and counterfactual values quantifies the “lost interest,” averaging %.1 points below where the market would have otherwise stood.
The sustained effect is reflected in β₃, indicating how the trend shifted as sports gradually returned, beginning with the NBA bubble in July 2020 and the NFL season in September 2020. This forms the delayed effect: an immediate crash followed by slow recovery. The current distance between actual and counterfactual lines shows whether the industry has fully re-aligned with its pre-pandemic path or if lingering deficits remain.